Simulating bicycle traffic by the Intelligent-Driver Model -- reproducing the traffic-wave characteristics observed in a bicycle-following experiment

TL;DR

This paper demonstrates that bicycle traffic flow can be effectively simulated using reparameterized car-following models, showing similar performance to models specifically designed for bikes, thus supporting the hypothesis of qualitative equivalence.

Contribution

It provides evidence that bicycle traffic dynamics can be modeled with car-following models, bridging the gap between vehicular and bicycle traffic flow modeling.

Findings

Both models show similar calibration and validation metrics.

Bicycle traffic can be described by reparameterized car-following models.

Supports the hypothesis of qualitative equivalence between vehicular and bicycle traffic dynamics.

Abstract

Bicycle traffic operations become increasingly important and yet are largely ignored in the traffic flow community, until recently. We hypothesize that there is no qualitative difference between vehicular and bicycle traffic flow dynamics, so the latter can be described by reparameterized car-following models. To test this proposition, we reproduce bicycle experiments on a ring with the Intelligent-Driver Model and compare its fit quality (calibration) and predictive power (validation) with that of the Necessary-Deceleration-Model which is specifically designed for bike traffic. We find similar quality metrics for both models, so the above hypothesis of a qualitative equivalence cannot be rejected.